clickzetta-table-stream-pipeline

Orchestrate Table Stream CDC pipelines with change_tracking and MERGE consumption.

8|3|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-table-stream-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clickzetta-table-stream-pipeline
Source: https://github.com/yunqiqiliang/clickzetta-skills/tree/main/clickzetta-table-stream-pipeline
Command: npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-table-stream-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates end-to-end Table Stream change data capture (CDC) pipelines to reliably track and deliver table changes for incremental ETL, auditing, and downstream processing across a data lake or warehouse.

Core Features & Use Cases

  • Enable source table change tracking and create Table Streams to capture INSERTS/UPDATES/DELETES
  • Support STANDARD and APPEND_ONLY modes to cover data synchronization and historical auditing
  • Provide idempotent consumption via MERGE to ensure no duplicates during ETL
  • Preview changes and manage offsets to facilitate fault-tolerant, resumable pipelines
  • Suitable for real-time data replication, incremental ETL, and audit trails across data platforms

Quick Start

Enable change_tracking on the source table, create a TABLE STREAM on the source, prepare a target, and consume via MERGE for an end-to-end CDC pipeline.

Frequently Asked Questions about clickzetta-table-stream-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an incremental ETL pipeline using change data capture?▼

To build an incremental ETL pipeline, enable change_tracking on the source table, create a TABLE STREAM to capture inserts, updates, and deletions, prepare the target table, and consume changes via MERGE for idempotent synchronization.

What is the difference between STANDARD and APPEND_ONLY modes in table stream CDC?▼

STANDARD mode captures all inserts, updates, and deletions for data synchronization, while APPEND_ONLY mode captures only new inserts, making it suitable for historical auditing and log-based data tracking without update overhead.

How can I prevent duplicate records during incremental ETL?▼

You can prevent duplicates during incremental ETL by applying offset-based consumption and using MERGE operations, which ensures idempotent data delivery and reliable synchronization even if the pipeline is rerun.

Can I preview table changes before committing them to my target data warehouse?▼

Yes, the pipeline supports data preview functionality, allowing you to inspect captured table stream changes before applying them, which facilitates fault-tolerant processing and ensures accurate downstream delivery.

How do I make a resumable CDC pipeline after a failure?▼

A resumable CDC pipeline is achieved through offset-based consumption and change_tracking management, allowing the pipeline to restart from the last processed offset without losing data integrity or duplicating records.

When should I use table stream CDC instead of full batch loads?▼

Use table stream CDC for near real-time data replication and incremental ETL when source tables change frequently, as it tracks only modifications rather than repeatedly processing entire datasets.